{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "加载太阳黑子数据，并提取训练数据，同时初始化相关参数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "ss_data = pd.read_csv(\"http://image.cador.cn/data/sunspot.csv\")\n",
    "\n",
    "# 使用后50年的数据进行验证，以前的数据用于建模\n",
    "rows = ss_data.shape[0]\n",
    "train_data = ss_data[0:(rows - 50)]\n",
    "n = train_data.shape[0]\n",
    "\n",
    "# 设置候选门限值\n",
    "thresholdV = train_data.sunspot.sort_values().values[np.int32(np.arange(30,70,3)/100*n)]\n",
    "\n",
    "#设置最大门限延迟量dmax、自回归最大阶数、默认最小AIC值\n",
    "dmax = 5\n",
    "pmax = 5\n",
    "minAIC = 1e+10"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "使用Python编写函数get_model_info，根据门限延迟量、自回归阶数、给定门限值以及相对于门限值的取值范围（门限值上方或下方），求得对应自回归模型的AIC和回归系数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 在指定门限延迟量、阶数及门限值的前提下，返回对应自回归模型AIC值和自回归系数\n",
    "def get_model_info(tsobj, d, p, r, isup=True):\n",
    "    if isup:\n",
    "        dst_set = np.where(tsobj > r)[0] + d\n",
    "    else:\n",
    "        dst_set = np.where(tsobj <= r)[0] + d\n",
    "    \n",
    "    tmpdata = None\n",
    "    \n",
    "    # 重建基础数据集\n",
    "    # xt=a0+a1*x(t-1)+...+ap*x(t-p)\n",
    "    for i in dst_set:\n",
    "        if i>p and i < len(tsobj):\n",
    "            if tmpdata is None:\n",
    "                tmpdata = [tsobj[(i-p):(i+1)]]\n",
    "            else:\n",
    "                tmpdata = np.r_[tmpdata, [tsobj[(i-p):(i+1)]]]\n",
    "    x = np.c_[[1]*tmpdata.shape[0],tmpdata[:,0:p]]\n",
    "    coef = np.matmul(np.matmul(np.linalg.inv(np.matmul(x.T,x)),x.T),tmpdata[:,p])\n",
    "    epsilon = tmpdata[:,p] - np.matmul(x,coef)\n",
    "    aic = tmpdata.shape[0]*np.log(np.var(epsilon))+2*(p+2)\n",
    "    return {\"aic\":aic, \"coef\":coef}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "基于AIC最小化，确定最优参数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1891.4713402264924\n",
      "1755.538487229318\n",
      "1752.083202344418\n",
      "1748.4711307472792\n",
      "1743.6129259864463\n",
      "1724.2526949091841\n",
      "1720.797410024284\n",
      "1717.1853384271453\n",
      "1712.3271336663124\n",
      "1712.300357533335\n",
      "1707.4421527725021\n",
      "1706.4238414115835\n",
      "1702.0727843232967\n",
      "1695.662681718808\n",
      "1691.3116246305212\n",
      "1690.1250329522081\n",
      "1677.9116575916719\n",
      "1673.560600503385\n",
      "1673.4468194316064\n",
      "1669.0957623433196\n",
      "1668.3446627440849\n",
      "1663.993605655798\n",
      "1660.5336253999255\n",
      "1655.6291639346096\n",
      "1655.4545406303598\n",
      "1649.7328339249132\n",
      "1649.0374785857164\n",
      "1641.6917037411604\n",
      "1641.6898272867143\n",
      "1633.7029050725023\n",
      "1633.7010286180562\n",
      "1628.4092096281329\n",
      "1628.4073331736868\n",
      "1617.777258646814\n",
      "1617.775382192368\n",
      "1614.0492523302578\n",
      "1613.7875399449235\n",
      "1612.4584851226264\n"
     ]
    }
   ],
   "source": [
    "# 选择最优参数\n",
    "for tsv in thresholdV:\n",
    "    for d in range(1,dmax+1):\n",
    "        for p1 in range(1,pmax+1): # <= r\n",
    "            model1 = get_model_info(train_data.sunspot.values, d, p=p1, r=tsv, isup=False)\n",
    "            for p2 in range(1,pmax+1): # > r \n",
    "                model2 = get_model_info(train_data.sunspot.values, d, p=p2, r=tsv, isup=True)\n",
    "                if model1['aic']+model2['aic'] < minAIC:\n",
    "                    minAIC = model1['aic']+model2['aic']\n",
    "                    a_tsv = tsv\n",
    "                    a_d = d\n",
    "                    a_p1 = p1\n",
    "                    a_p2 = p2\n",
    "                    coef1 = model1['coef']\n",
    "                    coef2 = model2['coef']\n",
    "                    print(minAIC)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "我们基于历史数据，逐步对1969~2018年的黑子数量进行预测，并与真实值比较"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import matplotlib\n",
    "# 以下 font.family 设置仅适用于 Mac系统，其它系统请使用对应字体名称\n",
    "matplotlib.rcParams['font.family'] = 'Arial Unicode MS'\n",
    "# 使用求出的参数，对后50年的数据逐年预测\n",
    "predsData = []\n",
    "\n",
    "for i in range(rows - 50,rows):\n",
    "    t0 = ss_data.sunspot.values[i - a_d]\n",
    "    if t0 <= a_tsv:\n",
    "        predsData.append(np.sum(np.r_[1,ss_data.sunspot.values[(i-a_p1):i]]*coef1))\n",
    "    else:\n",
    "        predsData.append(np.sum(np.r_[1,ss_data.sunspot.values[(i-a_p2):i]]*coef2))\n",
    "\n",
    "\n",
    "plt.figure(figsize=(10,6))\n",
    "plt.plot(range(rows)[-100:rows],ss_data.sunspot[-100:rows],'-',c='black',linewidth=2,label=\"真实值\")\n",
    "plt.plot(range(rows)[-50:rows],predsData,'b--',label=\"预测值\")\n",
    "plt.xticks(range(rows)[-100:rows][::15],ss_data.year[-100:rows][::15],rotation=50)\n",
    "plt.xlabel(\"$year$\",fontsize=15)\n",
    "plt.ylabel(\"$sunspot$\",fontsize=15)\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
